Table of Contents

• About Peter Lynch
• How It Works
  • Quick Research
  • Deep Research
  • Quick vs Deep
• Installation
  • Prerequisites
  • macOS
  • Windows
• Disclaimer
• License

About Peter Lynch

Peter Lynch ran the Fidelity Magellan Fund from 1977 to 1990, delivering an average annual return of 29.2%, beating the S&P 500 index — making it one of the best-performing mutual funds in history.

His investment philosophy is grounded in a simple but powerful idea: ordinary investors have a real edge when they pay attention to the world around them.

• Invest in what you understand — focus on businesses whose products, services, and revenue model you can clearly explain.
• Focus on fundamentals — prioritize earnings growth, financial health, and business quality over short-term market movements or predictions.
• Growth at a reasonable price (GARP) — seek companies that combine solid growth with sensible valuation.
• Think long-term — durable business performance matters far more than short-term market noise.
• Use your individual advantage — everyday observations can help you spot great investments before Wall Street does.

"The person that turns over the most rocks wins the game." — Peter Lynch

He is best known for the books: Learn to Earn, One Up on Wall Street, and Beating the Street.

I created this project to see whether AI could generate stock research reports inspired by Peter Lynch's investing philosophy. After a few months of analyzing his books, iterating on prompts, and building the application, it started producing surprisingly useful reports.

How It Works

Search a company by name or ticker, choose a research mode, and receive an AI-generated report saved to your history for later review.

⚡ Quick Research

Role of a fast screening report is to answer one question: "Is this stock worth deeper research?"

Prompts used for Quick Research:
Quick Research Core Methodology
Quick Research System Prompt

Example Quick Research reports:

• Example 1: The Coca-Cola Company
• Example 2: 3M Company
• Example 3: Microsoft Corporation

🔬 Deep Research

A comprehensive report designed to deeply understand the stock.

Prompts used for Deep Research:

• Deep Research Core Methodology
• Deep Research System Prompt

Example Deep Research report:

• Example 1: Microsoft Corporation

Quick vs Deep

Installation

Prerequisites

macOS

# 1. Clone & enter project
git clone <repo-url>
cd fundamentalio

# 2. Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate

# 3. Install dependencies
pip install --upgrade pip && pip install -r requirements.txt

# 4. Configure environment
cp .env.example .env        # open .env and fill in your API keys

# 5. Set up database and frontend
python manage.py migrate
python manage.py tailwind install

Start the app (two terminals):

# Terminal 1 — Django dev server
source venv/bin/activate && python manage.py runserver

# Terminal 2 — Tailwind CSS watcher
source venv/bin/activate && python manage.py tailwind start

🌐 Open http://127.0.0.1:8000

Windows

# 1. Clone & enter project
git clone <repo-url>
cd fundamentalio

# 2. Create and activate virtual environment
python -m venv venv
venv\Scripts\activate

# 3. Install dependencies
pip install --upgrade pip && pip install -r requirements.txt

# 4. Configure environment
copy .env.example .env      # open .env and fill in your API keys

# 5. Set up database and frontend
python manage.py migrate
python manage.py tailwind install

Start the app (two terminals):

# Terminal 1 — Django dev server
venv\Scripts\activate; python manage.py runserver

# Terminal 2 — Tailwind CSS watcher
venv\Scripts\activate; python manage.py tailwind start

🌐 Open http://127.0.0.1:8000

Disclaimer

1. This project is an independent, open-source tool inspired by the investing principles popularized by Peter Lynch.
2. It is not affiliated with, endorsed by, sponsored by, or associated with Peter Lynch, Fidelity Investments, or any related organization.
3. All trademarks, names, and references belong to their respective owners and are used for educational and informational purposes only.
4. This project uses the yfinance library. Yahoo! finance API is intended for personal use only. Go to yfinance for more info.
5. Reports are AI-generated and may contain errors or inaccuracies. Always verify information independently before acting on it.
6. This does not constitute financial, investment, or trading advice. Users are solely responsible for their own investment decisions.

Star ⭐️

If you find this project useful, please consider starring it — you'll find it quickly later, and it helps others discover it too.

Quick note

This repository contains a simplified and modified version of a production-like project I built while learning web application development.

License

Distributed under the MIT License — see LICENSE for details.

_{Created by Szymon Nycz · Inspired by Peter Lynch's philosophy}